This paper presents an investigative work about the application of State Space Model-Based Predictive Control in a three-phase Permanent Magnet Synchronous Motor with trapezoidal back-electromotive force, for speed control. Such motor is utilized in the white goods appliances industry and also in automotive and medical applications, among others, especially due to its high effiency and long life cycle. The predictive control methods present a differential in the driving performance for industrial applications, mainly by enabling the imposition of constraints. In this work, a linear prediction model identified with a Least Mean Squares algorithm is used with the State-Space predictive control approach. Such predictive method is interesting for industrial applications for being easy to tune and, in addition to the imposition of constraints, allowing ponderation between tracking performance and spent energy. The utilization of constraints is discussed for the predictive algorithm in this work. There are satisfactory simulated and experimental results that show advantages in using the mentioned control method to drive the Permanent Magnet Synchronous Motor.

-This paper presents a predictive control approach for speed control of a permanent magnet synchronous motor with trapezoidal back-electromotive force drive.The prediction model was numerically identified and considers existent transport delays in the drive. The proposed technique operates with sixstep and pulse-width modulations, which are normally used in proportional-integrative control structures. A computational cost analysis was also done. Results show improvements in speed performance, comparing to tested proportional-integral control.

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One of the main reasons that prevent the use of model-based predictive control (MPC) in power electronics and motor drive is the MPC design. The processes related to these fields have fast dynamics, and most of the MPC design guides involve the control of slow dynamic processes, which allow high sampling times and, consequently, a large time for the control action calculation. Moreover, practically only the linear model-based predictive controller can have small computational cost, which causes difficulty for MPC application in nonlinear processes as electrical motors. Therefore, this paper presents a MPC design theory for first-order control models, as the brushless direct current (BLDC) motor drive, assuming direct speed control (without current inner loop). Thus, the MPC cost function tune is obtained according to prediction model parameters, the study of optimization gain curves and, optionally, an extended closed-loop root locus analysis. To deal with the motor nonlinearities, both multi-model approach and six-step modulation are employed. This paper also discusses current peak transients, salient pole BLDC motor modeling, integral action included in the MPC control action and solutions for Hall effect sensor nonlinear speed measurement. Simulation and experimental results confirm the proposed design theory as well as the other discussed issues.

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